@article{
 title = {Tissue-type mapping of gliomas},
 type = {article},
 year = {2019},
 keywords = {Glioma,Magnetic resonance spectroscopy (MRS),Multimodal MRI,Nosologic imaging,Pattern recognition},
 pages = {101648},
 volume = {21},
 websites = {https://doi.org/10.1016/j.nicl.2018.101648},
 publisher = {Elsevier},
 id = {262a215c-aba9-35e4-b424-262cb2c2b8c0},
 created = {2024-01-13T06:15:55.531Z},
 file_attached = {true},
 profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},
 last_modified = {2024-01-13T06:23:29.302Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Purpose: To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. Materials and methods: We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1 H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T 2 -weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. Results: Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1 H MRS characteristics. Conclusions: 1 H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.},
 bibtype = {article},
 author = {Raschke, Felix and Barrick, Thomas R. and Jones, Timothy L. and Yang, Guang and Ye, Xujiong and Howe, Franklyn A.},
 doi = {10.1016/j.nicl.2018.101648},
 journal = {NeuroImage: Clinical},
 number = {July 2018}
}

{"_id":"ELaGu2NibQtfTGJdi","bibbaseid":"raschke-barrick-jones-yang-ye-howe-tissuetypemappingofgliomas-2019","author_short":["Raschke, F.","Barrick, T., R.","Jones, T., L.","Yang, G.","Ye, X.","Howe, F., A."],"bibdata":{"title":"Tissue-type mapping of gliomas","type":"article","year":"2019","keywords":"Glioma,Magnetic resonance spectroscopy (MRS),Multimodal MRI,Nosologic imaging,Pattern recognition","pages":"101648","volume":"21","websites":"https://doi.org/10.1016/j.nicl.2018.101648","publisher":"Elsevier","id":"262a215c-aba9-35e4-b424-262cb2c2b8c0","created":"2024-01-13T06:15:55.531Z","file_attached":"true","profile_id":"d5b9f595-4104-33d2-b640-e84dee1b7ded","last_modified":"2024-01-13T06:23:29.302Z","read":false,"starred":false,"authored":"true","confirmed":"true","hidden":false,"private_publication":false,"abstract":"Purpose: To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. Materials and methods: We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1 H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T 2 -weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. Results: Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1 H MRS characteristics. Conclusions: 1 H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.","bibtype":"article","author":"Raschke, Felix and Barrick, Thomas R. and Jones, Timothy L. and Yang, Guang and Ye, Xujiong and Howe, Franklyn A.","doi":"10.1016/j.nicl.2018.101648","journal":"NeuroImage: Clinical","number":"July 2018","bibtex":"@article{\n title = {Tissue-type mapping of gliomas},\n type = {article},\n year = {2019},\n keywords = {Glioma,Magnetic resonance spectroscopy (MRS),Multimodal MRI,Nosologic imaging,Pattern recognition},\n pages = {101648},\n volume = {21},\n websites = {https://doi.org/10.1016/j.nicl.2018.101648},\n publisher = {Elsevier},\n id = {262a215c-aba9-35e4-b424-262cb2c2b8c0},\n created = {2024-01-13T06:15:55.531Z},\n file_attached = {true},\n profile_id = {d5b9f595-4104-33d2-b640-e84dee1b7ded},\n last_modified = {2024-01-13T06:23:29.302Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Purpose: To develop a statistical method of combining multimodal MRI (mMRI) of adult glial brain tumours to generate tissue heterogeneity maps that indicate tumour grade and infiltration margins. Materials and methods: We performed a retrospective analysis of mMRI from patients with histological diagnosis of glioma (n = 25). 1 H Magnetic Resonance Spectroscopic Imaging (MRSI) was used to label regions of “pure” low- or high-grade tumour across image types. Normal brain and oedema characteristics were defined from healthy controls (n = 10) and brain metastasis patients (n = 10) respectively. Probability density distributions (PDD) for each tissue type were extracted from intensity normalised proton density and T 2 -weighted images, and p and q diffusion maps. Superpixel segmentation and Bayesian inference was used to produce whole-brain tissue-type maps. Results: Total lesion volumes derived automatically from tissue-type maps correlated with those from manual delineation (p < 0.001, r = 0.87). Large high-grade volumes were determined in all grade III & IV (n = 16) tumours, in grade II gemistocytic rich astrocytomas (n = 3) and one astrocytoma with a histological diagnosis of grade II. For patients with known outcome (n = 20), patients with survival time < 2 years (3 grade II, 2 grade III and 10 grade IV) had a high-grade volume significantly greater than zero (Wilcoxon signed rank p < 0.0001) and also significantly greater high grade volume than the 5 grade II patients with survival >2 years (Mann Witney p = 0.0001). Regions classified from mMRI as oedema had non-tumour-like 1 H MRS characteristics. Conclusions: 1 H MRSI can label tumour tissue types to enable development of a mMRI tissue type mapping algorithm, with potential to aid management of patients with glial tumours.},\n bibtype = {article},\n author = {Raschke, Felix and Barrick, Thomas R. and Jones, Timothy L. and Yang, Guang and Ye, Xujiong and Howe, Franklyn A.},\n doi = {10.1016/j.nicl.2018.101648},\n journal = {NeuroImage: Clinical},\n number = {July 2018}\n}","author_short":["Raschke, F.","Barrick, T., R.","Jones, T., L.","Yang, G.","Ye, X.","Howe, F., A."],"urls":{"Paper":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded/file/6fc38dd8-1f56-e61f-610b-950f68dd40b7/1_s20_S2213158218303966_main.pdf.pdf","Website":"https://doi.org/10.1016/j.nicl.2018.101648"},"biburl":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded","bibbaseid":"raschke-barrick-jones-yang-ye-howe-tissuetypemappingofgliomas-2019","role":"author","keyword":["Glioma","Magnetic resonance spectroscopy (MRS)","Multimodal MRI","Nosologic imaging","Pattern recognition"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://bibbase.org/service/mendeley/d5b9f595-4104-33d2-b640-e84dee1b7ded","dataSources":["HEfSqjnnZJSbmz8ro","2252seNhipfTmjEBQ"],"keywords":["glioma","magnetic resonance spectroscopy (mrs)","multimodal mri","nosologic imaging","pattern recognition"],"search_terms":["tissue","type","mapping","gliomas","raschke","barrick","jones","yang","ye","howe"],"title":"Tissue-type mapping of gliomas","year":2019}